In recent years, for the purposes of quick recognition of the state of occurrence of defects on a surface of a wafer represented by a semiconductor integrated circuit and monitoring of the number of the occurred defects for each type of the defects, there have been developed technologies of taking an image of a defect portion for automatic classification.
One of the technologies of performing the automatic classification from image data of the defect portion is a technology called learning classification. In the learning classification technology, image data for learning is collected in advance and learned, to thereby optimize a classification model. Representative methods of the learning classification technology include discriminant analysis based on a neural network and the Bayes discriminant theory and the like.
Another technology of the automatic classification from the image data of the defect portion is a technology called a rule-based classification. In the rule-based classification technology, attribute data is extracted from image data, and a value of the attribute data is determined from an “IF-THEN” rule incorporated in a system, to thereby classify defects.
Further, in Patent Literature 1, a method of improving classification performance by combining a plurality of classification models is described. In the method described in Patent Literature 1, a classification model formed of a hierarchy of multiple levels of rough defect classification called main classes and detailed defect classification called sub classes is used.